Api Rate Abuse in Adonisjs with Mssql

AdonisJS is a Node.js web framework that encourages a consistent routing and controller pattern. When building endpoints backed by Microsoft SQL Server via an Mssql client (e.g., using an mssql pool), improper rate controls can expose abusive patterns. Without explicit limits, an unauthenticated or weakly limited endpoint can be hammered, creating high load on the database and enabling enumeration or denial-of-service via repeated queries.

Rate abuse in this stack often maps to the BFLA/Privilege Escalation and Rate Limiting checks in middleBrick’s scan. For example, an endpoint like /api/users/:id/profile that accepts a user ID and queries Mssql without per-IP or per-account throttling can be exploited to enumerate valid user IDs (BOLA/IDOR) and strain the Mssql instance with repetitive, costly requests.

Consider a typical AdonisJS controller that queries Mssql using the @mssql/mssql package:

const sql = require('@mssql/mssql');

async function getUserProfile(req, res) {
  const pool = await sql.connect(process.env.MSSQL_CONNECTION_STRING);
  const result = await pool.request()
    .input('id', sql.Int, req.params.id)
 .query('SELECT id, username, email FROM users WHERE id = @id');
  res.json(result.recordset[0] || {});
}

If this route lacks rate limiting, an attacker can send many requests with different IDs, causing the Mssql server to execute similar parameterized queries repeatedly. This can lead to high CPU and memory usage on the database, and may expose information through timing differences or error messages. middleBrick’s Rate Limiting check would flag this lack of throttling and highlight the potential for abuse in combination with IDOR-like inputs.

Another common pattern is a search or list endpoint that queries Mssql with a filter but does not cap the returned set or throttle frequent calls:

async function searchUsers(req, res) {
  const pool = await sql.connect(process.env.MSSQL_CONNECTION_STRING);
  const { q } = req.query;
  const result = await pool.request()
    .input('q', sql.NVarChar, `%${q}%')
    .query('SELECT id, username FROM users WHERE username LIKE @q');
  res.json(result.recordset);
}

Frequent calls with different q values can cause heavy reads on Mssql, especially if relevant columns are not indexed properly. This endpoint may also be susceptible to enumeration if error responses differ based on whether partial matches exist. middleBrick’s checks for Rate Limiting and Property Authorization help surface these risks by correlating runtime behavior with the OpenAPI spec and observed responses.

To mitigate, you should enforce limits at the application or gateway level and ensure Mssql queries are efficient and safe against repeated abuse. This includes using parameterized queries to avoid injection, applying appropriate indexes, and adding concurrency or frequency caps per identity or IP.

Remediation centers on adding explicit rate limiting, validating inputs, and optimizing queries when using Mssql with AdonisJS. Below are concrete patterns you can adopt.

• Use a token-bucket or fixed-window limiter per IP or per user. In AdonisJS, you can implement middleware to track requests and enforce thresholds before hitting Mssql:

// middleware/RateLimit.js
const mssql = require('@mssql/mssql');
const rateLimit = new Map();

async function rateLimitMiddleware(req, res, next) {
  const key = req.ip; // or req.authUser.id for authenticated limits
  const now = Date.now();
  const windowMs = 60_000; // 1 minute
  const maxRequests = 60;

  if (!rateLimit.has(key)) {
    rateLimit.set(key, { count: 1, start: now });
  } else {
    const state = rateLimit.get(key);
    if (now - state.start > windowMs) {
      state.count = 1;
      state.start = now;
    } else {
      state.count += 1;
    }
    if (state.count > maxRequests) {
      return res.status(429).json({ error: 'Too many requests' });
    }
  }
  next();
}

// In routes file
Route.get('/api/users/:id/profile', rateLimitMiddleware, async (req, res) => {
  const pool = await mssql.connect(process.env.MSSQL_CONNECTION_STRING);
  const result = await pool.request()
    .input('id', mssql.Int, req.params.id)
    .query('SELECT id, username, email FROM users WHERE id = @id');
  res.json(result.recordset[0] || {});
});

• Parameterize all inputs and avoid dynamic SQL to prevent injection and ensure query plan reuse:

async function getUserById(pool, userId) {
  const result = await pool.request()
    .input('userId', mssql.Int, userId)
    .query('SELECT id, username, email FROM users WHERE id = @userId');
  return result.recordset[0];
}

• Add appropriate database indexes to support filtered queries and reduce I/O under repeated load:

-- Mssql index example to support username searches
CREATE NONCLUSTERED INDEX IX_users_username ON users (username) WHERE username IS NOT NULL;

• For endpoints returning sensitive or high-volume data, enforce caps on result size and add pagination:

async function searchUsers(req, res) {
  const pool = await mssql.connect(process.env.MSSQL_CONNECTION_STRING);
  const { q, page = 1, limit = 20 } = req.query;
  const offset = (page - 1) * limit;
  const result = await pool.request()
    .input('q', mssql.NVarChar, `%${q}%`)
    .input('limit', mssql.Int, limit)
    .input('offset', mssql.Int, offset)
    .query('SELECT id, username FROM users WHERE username LIKE @q ORDER BY id OFFSET @offset ROWS FETCH NEXT @limit ROWS ONLY');
  res.json(result.recordset);
}

These patterns reduce the surface for rate abuse by controlling request frequency, ensuring efficient and safe database interactions, and minimizing the risk of enumeration or resource exhaustion when using Mssql with AdonisJS.